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1) I asked OpenAI what kind of web application I should make to help make data analysts more efficient.
It responded by telling me to build an app using NLP to provide people with Excel formulas based on a given prompt.
2) I told OpenAI the idea in a separate API request and asked it for an available domain name.
It gave me www.excelformulabot.com, which I built.
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I have a dataset of tagged and linked object bounding-boxes in sequential video frames. If that isn't clear, you can watch a demo here:
https://www.youtube.com/watch?v=QKxSzFaHsbc
For various reasons, it's possible that a trajectory could be 'broken' in the dataset. Quick visual scanning doesn't allow detection of a break in a single trajectory; there are so many horizontal links, it's tough to notice one of them being missing.
How would you economically eliminate a small percentage of breaks in trajectories?
Some things I've thought of:
* Bootstrapping, i.e. using a trained network to predict -> this is a bit complex, it's possible but not my first choice
* Build a tool to view all linked detections overlaid in a single frame (doesn't immediately identify broken trajectories, but it might help)
Is there any simple UI I can build to easily identify broken trajectories in the dataset?
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The Python package {copent} v0.3 now available on PyPI, with the new function 'mvnt' that implements the method for estimating the copula entropy-based statistic for multivariate normality test. See arXiv:2206.05956 for more details.
GITHUB: https://github.com/majianthu/pycopent
PyPI: https://pypi.org/project/copent/
Your comments are welcome.
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Here is the list of all >1,200 ICML 2022 (International Conference on Machine Learning) papers, and a highlight for each of them. ICML 2022 will take place from July 17 at Baltimore.
https://www.paperdigest.org/2022/07/icml-2022-highlights/
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UC Berkeley and Google AI Researchers Introduce ‘Director’: a Reinforcement Learning Agent that Learns Hierarchical Behaviors from Pixels by Planning in the Latent Space of a Learned World Model. The world model Director builds from pixels allows effective planning in a latent space. To anticipate future model states given future actions, the world model first maps pictures to model states. Director optimizes two policies based on the model states’ anticipated trajectories: Every predetermined number of steps, the management selects a new objective, and the employee learns to accomplish the goals using simple activities. The direction would have a difficult control challenge if they had to choose plans directly in the high-dimensional continuous representation space of the world model. To reduce the size of the discrete codes created by the model states, they instead learn a goal autoencoder. The goal autoencoder then transforms the discrete codes into model states and passes them as goals to the worker after the manager has chosen them.
✅ Director agent learns practical, general, and interpretable hierarchical behaviors from raw pixels
✅ Director successfully learns in a wide range of traditional RL environments, including Atari, Control Suite, DMLab, and Crafter
✅ Director outperforms exploration methods on tasks with sparse rewards, including 3D maze traversal with a quadruped robot from an egocentric camera and proprioception
Continue reading| Checkout the paper and project
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I would like to share personal insights about doing great research and towards being a globally leading researcher:
Not all our research legacies are correct or will be corrected shortly, so just keep taking the initiative to correct them. https://openreview.net/forum?id=xENf4QUL4LW¬eId=C2eCHs2k6CM.
Not all our papers get cited or published, so when our papers serve as a great foundation for other works, just keep positive and confident to deliver them to more people who may be interested.
Reddit discussion
Linkedin discussion
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CUHK released aDeepFashion-MultiModal dataset with rich multi-modal annotations, including manually annotated human parsing labels, manually annotated human keypoints, manually annotated fine-grained labels and textual descriptions in June 2022. Since then, researchers have been looking to work with the dataset, fine-tune it with CLIP model and different metrics.
While finetuning I understand is an imp. process and a difficult one, they claim to have gained 217% Delta increase on Recall metric. When I have been trying to run it, my laptop has not been so capable to run this, so I am looking for alternative for remote GPU.
But, is this growth of 217% from pertained to fine-tuned model even possible? A bit hard to believe. If so, is Colab a good option to run remote GPU while being able to make use of the functionality?
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A detailed and insightful study by MetaAI team on the memorization, overfitting and forgetting in LLMs.
The paper talks about how different definitions of "memorization" and how scaling affects the amount of training data that the large language models can memorize during the training phase. Studies are also presented on how the forgetting curves look like and how overfitting relates to memorization for these large language models. The Appendix section is a gold mine as well.
Annotated version of the paper - Github Link
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A detailed and insightful study by MetaAI team on the memorization, overfitting and forgetting in LLMs.
The paper talks about how different definitions of "memorization" and how scaling affects the amount of training data that the large language models can memorize during the training phase. Studies are also presented on how the forgetting curves look like and how overfitting relates to memorization for these large language models. The Appendix section is a gold mine as well.
Annotated version of the paper - Github Link
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Hi folks,
I was working on a personal experimental project, which I thought of making it open source now. It saves much time for literature research.
If you are an industrial researcher or in academia, you probably spend much time reading research articles and news related to your topic.
If you try to search papers related to your topic, finding relevant documents on the internet takes time. You probably know the pain of extracting citations of articles from different websites.
Previously I used to fetch papers from google or semantic scholar, but semantic scholar does not show correct paper citations.
I am excited to announce RESP: Research Papers Search
Features:
Fetch all citations of a single paper from Google Scholar in CSV format
Fetch all related papers of a single paper from Google Scholar in CSV format
Fetch all connected papers from connectedpapers.com (it does not use a citation tree, it uses similarity to build graphs) in CSV format
Fetch relevant papers based on keywords from different sources, including Arxiv, ACL, ACM, PMLR, NeurIPS, cvf etc., in CSV format
GITHUB: https://github.com/monk1337/resp
Examples: https://github.com/monk1337/resp/tree/main/examples
I hope it will be helpful in your research. Thanks :)
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Today, social media is a huge source of news. Users rely on platforms like Facebook and Twitter to consume news. For certain industries such as insurance companies, first respondents, law enforcement, and government agencies, being able to quickly process news about relevant events occurring can help them take action while these events are still unfolding. […]
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Australian animator Marko Matosevic is taking jokes from a children’s school dads’ group and breathing them into animated life with NVIDIA Omniverse, a virtual world simulation and collaboration platform for 3D workflows.
The post Meet the Omnivore: Animator Entertains and Explains With NVIDIA Omniverse appeared first on NVIDIA Blog.
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AI Weirdness: the strange side of machine learning
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Image dump 1
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This is a guest blog post by Danny Brock, Rajeev Govindan and Krishnaram Kenthapadi at Fiddler AI. Your Amazon SageMaker models are live. They’re handling millions of inferences each day and driving better business outcomes for your company. They’re performing exactly as well as the day they were launched. Er, wait. Are they? Maybe. Maybe […]
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Data scientists often work towards understanding the effects of various data preprocessing and feature engineering strategies in combination with different model architectures and hyperparameters. Doing so requires you to cover large parameter spaces iteratively, and it can be overwhelming to keep track of previously run configurations and results while keeping experiments reproducible. This post walks […]
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Organizations are increasingly building and using machine learning (ML)-powered solutions for a variety of use cases and problems, including predictive maintenance of machine parts, product recommendations based on customer preferences, credit profiling, content moderation, fraud detection, and more. In many of these scenarios, the effectiveness and benefits derived from these ML-powered solutions can be further […]
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Sponsored Post If you’re a data engineer or data scientist, you know how hard it is to generate and maintain realistic data at scale. And to guarantee data privacy protection, in addition to all your day-to-day responsibilities? OOF. Talk about a heavy lift. But in today’s world, efficient data de-identification is no longer optional for […]
The post High-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike appeared first on Machine Learning Mastery.
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Check out our new open source code editor for transforming data and building ML pipelines: https://github.com/mage-ai/mage-ai
If you’re available, I’d love to hop on a quick Zoom to help you get set up.
In the meantime, here is the install guide: https://github.com/mage-ai/mage-ai#using-pip and a short tutorial: https://github.com/mage-ai/mage-ai/blob/master/docs/tutorials/train_titanic_model/README.md
I’d love to get your feedback on whether this is useful to you or not. Thank you so much!
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As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as
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Precision agriculture has recently shown a lot of interest in computer vision technology. Computer vision, at the heart of robotics and…
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Investigate the ultimate truth this GFN Thursday with Loopmancer, now streaming to all members on GeForce NOW. Stuck in a death loop, RTX 3080 and Priority members can search for the truth with RTX ON — including NVIDIA DLSS and ray-traced reflections. Plus, players can enjoy the latest Genshin Impact event with the “Summer Fantasia” Read article >
The post Action on Repeat: GFN Thursday Brings Loopmancer With RTX ON to the Cloud appeared first on NVIDIA Blog.
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Announcements Achieving endpoint visibility to ward off the threat of a breach has never been more important than it is in the age of data proliferation and hybrid workplaces. Multiple endpoints and locations heighten that risk, making it essential for CISOs and IT security teams to overcome common challenges. Find out how organizations can reach… Read More »DSC Weekly 12 July 2022: The Emergence of the Modern Studio Model
The post DSC Weekly 12 July 2022: The Emergence of the Modern Studio Model appeared first on Data Science Central.
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Twitter thread:
https://twitter.com/karpathy/status/1547332300186066944
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It looks like chronic kidney disease diagnosis has been solved in this paper: https://ieeexplore.ieee.org/document/8693581
I mean no disrespect to the authors, but this publication makes me slightly doubt the peer-review system. Or I am just such an amateur, that I am not seeing the brilliance behind this paper, which is also possible.
Have a read through it yourselves
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SimSwap (https://github.com/neuralchen/SimSwap) is basically a framework that carries out face-swapping in a similar way deepfake technology does with a source and a target video. However, for the source, only one image is required. Not sure how this would work since 1 image isn't enough for actual training. Is this simply face mapping? I feel like the output is a bit too sophisticated for that.
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In this seminar Aditya introduces a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. Watch on YouTube.
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Artificial intelligence (AI) has become synonymous with assistance and efficiency. From a technology that was looked at with mistrust as…
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https://preview.redd.it/xjtcha3r35b91.png?width=1298&format=png&auto=webp&s=00873223c1ea0c6afcd5e22c7645521036b7e341
This post presents a way to run transformers models via the Python C API. The referenced notebook loads two txtai workflows, one that translates English to French and another that summarizes a webpage. After loading the models through C code, another example runs the workflows through assembly to show this works with any native code.
Full code links: Notebook | GitHub
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BigScience Project introduces BLOOM (BigScience Large Open-science Open-access Multilingual Language Model), the first multilingual Large Language Model (LLM) trained in complete transparency by the largest group of AI academics. Unlike the traditional secrecy of industrial AI research laboratories, the project demonstrates the possibility of training promising AI models published by the larger research community responsibly and openly.
✅ Transformers-based LLM
✅ 176B parameters (larger than GPT-3 and OPT-175B)
✅ Trained on 1.6TB text data, the equivalent of 320 times the complete works of Shakespeare
Continue reading | Download
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Touring vehicles just became a little more grand. Electric vehicle maker Human Horizons provided a detailed glimpse earlier this month of its latest production model, the GT HiPhi Z. The intelligent EV is poised to redefine the grand tourer category with innovative, software-defined capabilities that bring luxurious cruising to the next level. The vehicle’s marquee Read article >
The post Grand Entrance: Human Horizons Unveils Smart GT Built on NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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Kristel Michielsen was into quantum computing before quantum computing was cool. The computational physicist simulated quantum computers as part of her Ph.D. work in the Netherlands in the early 1990s. Today, she manages one of Europe’s largest facilities for quantum computing, the Jülich Unified Infrastructure for Quantum Computing (JUNIQ) . Her mission is to help Read article >
The post Merge Ahead: Researcher Takes Software Bridge to Quantum Computing appeared first on NVIDIA Blog.
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Visual effects savant Surfaced Studio steps In the NVIDIA Studio this week to share his clever film sequences, Fluid Simulation and Destruction, as well as his creative workflows. These sequences feature quirky visual effects that Surfaced Studio is renowned for demonstrating on his YouTube channel.
The post Sequences That Stun: Visual Effects Artist Surfaced Studio Arrives ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
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Web analytics tools offer vital insights into your website’s visitors’ behavior by tracking their real-time activities on the platform from behind. These tools study almost everything – the number of daily and regular visitors, sessions and duration, conversions, and beyond. You can access a comprehensive report covering every aspect and personalize it to focus on… Read More »Web Analytics Dashboards Carry a World of Data for Various Purposes
The post Web Analytics Dashboards Carry a World of Data for Various Purposes appeared first on Data Science Central.
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Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed and they are neither magical nor hard to understand. In this tutorial, we will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How […]
The post Understanding the Design of a Convolutional Neural Network appeared first on Machine Learning Mastery.
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Scrapy is highly customizable and developer friendly crawling framework in Python. It can help you build in few line wonderful crawler to…
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A re-implementation of the famous 2020 paper - "Extracting Training Data from Large Language Models" by Nicholas Carlini, Florian Tramer et al.
Code - https://github.com/shreyansh26/Extracting-Training-Data-from-Large-Langauge-Models
The official implementation is great and I definitely learned a few things from it. In the re-implementation, I have also included the temperature-decay sampling and sliding-window-based minimum perplexity metric which was not present in the official implementation.
I checked the extracted Samples (refer to the Github repo) and they surely contained some memorized information.
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An awesome collection of Federated learning & Blockchain research papers in the Healthcare domain.
Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy preservation. This repo contains a curated list of Federated Learning papers/resources and recent advancements in Healthcare.
As of now ~330 papers
Pr's welcome
https://github.com/monk1337/Aweome-Heathcare-Federated-Learning
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The unveiling by U.S. President Joe Biden Monday of the first full-color image from the James Webb Space Telescope is already astounding — and delighting — humans around the globe. “We can see possibilities nobody has ever seen before, we can go places nobody has ever gone before,” Biden said during a White House press Read article >
The post AI on the Sky: Stunning New Images From the James Webb Space Telescope To Be Analyzed by, Train, AI appeared first on NVIDIA Blog.
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Engineers are using the NVIDIA Omniverse 3D simulation platform as part of a proof of concept that promises to become a model for putting green energy to work around the world. Dubbed Gigastack, the pilot project — led by a consortium that includes Phillips 66 and Denmark-based renewable energy company Ørsted — will create low-emission Read article >
The post Windfall: Omniverse Accelerates Turning Wind Power Into Clean Hydrogen Fuel appeared first on NVIDIA Blog.
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LiDAR is a key enabling technology in growing autonomous markets, such as robotics, industrial, infrastructure, and automotive. LiDAR delivers precise 3D data about its environment in real time to provide “vision” for autonomous solutions. For autonomous vehicles (AVs), nearly every carmaker uses LiDAR to augment camera and radar systems for a comprehensive perception stack capable […]
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We live in a data-rich world. Very data rich. Indeed, it’s estimated that roughly 2.5 quintillion bytes of data are created every day. Perhaps because of its ubiquity, there are those who believe the sheer volume of available data means we have all we need to easily and accurately answer any question without delay. If… Read More »Why We Need to Move From Data-First to a Knowledge-First World
The post Why We Need to Move From Data-First to a Knowledge-First World appeared first on Data Science Central.
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The tech industry is abuzz with hyped up pontifications and bold predictions of the business-changing potential of Data Products. I could not be happier as it’s a topic I have explored in several blogs (see the end of this blog for a list of my blogs on Data Products…yea, I know, get a life). A… Read More »Critical Role of Analytic Profiles in Developing Data Products
The post Critical Role of Analytic Profiles in Developing Data Products appeared first on Data Science Central.
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According to the McKinsey Report called Value Creation in the Metaverse: $120b+ in investment has flowed into the metaverse so far in 2022 79% of consumers active on the metaverse have made a purchase >15% of corporate revenue is expected to come from the metaverse in the next 5 years according to 25% of senior… Read More »Metaverse use cases – Which industries could the metaverse impact?
The post Metaverse use cases – Which industries could the metaverse impact? appeared first on Data Science Central.
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Executing Industrial Internet of Things (IIoT) solutions is vital as the most competitive global manufacturing companies are becoming digital enterprises. Industrial Internet of Things (IIoT) solutions and platforms are leading the reshaping and transformation of landscapes. A pre-built Industrial Internet of Things (IIoT) solution offers the benefit of a ready-made “IoT development kit” with the… Read More »Features of IIoT (Industrial Internet of Things) Seamless Connectivity and Data Acquisition
The post Features of IIoT (Industrial Internet of Things) Seamless Connectivity and Data Acquisition appeared first on Data Science Central.
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On Wednesday, July 13th at 11 am EST, please join DQLabs for an exclusive virtual event“Defining Data Relevance: The rise of the Modern Data Stack and the Modern Data Quality Platform”. The data producers, consumers, and leaders deserve an ecosystem that delivers the data that is relevant to them – one size fits all approaches… Read More »Webinar Series -The rise of the Modern DataStack and the Modern Data Quality Platform
The post Webinar Series -The rise of the Modern DataStack and the Modern Data Quality Platform appeared first on Data Science Central.
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IT leaders are running into several RPA failures. Here, we have covered the top 7 reasons why RPA implementations fail and how you can…
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Crawling a website is as today an essential skill for anyone working in or with the digital industry. Firstly, I will start by clarifying…
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Google Imagen: a machine learning system that can generate graphics from text input.
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https://reddit.com/link/vw3tkf/video/xe0t4pumpta91/player
https://reddit.com/link/vw3tkf/video/7pf9dl3npta91/player
Programming
Function from Description
Code to Explanation
Fix invalid Code
Translate Languages
Class from Description
Get Language from Code
Function from Docstring
Helpers
Regex from Description
Regex to Explanation
Linux Command
Get time complexity
Git Command from Description
Database
Text Description to SQL Command
Web
Generate HTML from Description
CSS from Description
Meta Tags from Description
I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here:
programming-helper.com
Have fun using the tool ❤️
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Hello, the title says it all. I'm trying to find any ressources (mainly aligned corpus) that could be helpful in identifying and simplifying complex sentences in French. ALECTOR is the only one I stumbled upon.
Do you have any resources or tips? I was wondering if searching for book and their simplified version could be useful but I fear it would be more like learning to translate old french into modern french.
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In cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy. However, some recent empirical studies demonstrate that with proper input representation and hyper-parameter tuning, multi-agent PG can achieve surprisingly strong performance compared to off-policy VD methods.
Why could PG methods work so well? In this post, we will present concrete analysis to show that in certain scenarios, e.g., environments with a highly multi-modal reward landscape, VD can be problematic and lead to undesired outcomes. By contrast, PG methods with individual policies can converge to an optimal policy in these cases. In addition, PG methods wit…
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For several years, the Stratego board game has been regarded as one of the most promising areas of research in Artificial Intelligence. Stratego is a two-player board game in which each player attempts to take the other player’s flag. There are two main challenges in the game. 1) There are 10535 potential states in the Stratego game tree. 2) Each player in this game must consider 1066 possible deployments at the beginning of the game. Due to the various complex components of the game’s structure, the AI research community has made minimal progress in this area.
This research introduces DeepNash, an autonomous agent that can develop human-level expertise in the imperfect information game Stratego from scratch. Regularized Nash Dynamics (R-NaD), a principled, model-free reinforcement learning technique, is the prime backbone of DeepNash. DeepNash achieves an ε-Nash equilibrium by integrating R-NaD with deep neural network architecture. A Nash equilibrium ensures that the agent will perform well even when faced with the worst-case scenario opponent. The stratego game and a description of the DeepNash technique are shown in Figure 1.
Continue reading | Checkout the paper
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In this research article, the researchers from UC Berkeley demonstrated that extracting from a sizable news corpus may effectively train language models on prior predicting problems.
Forecasting is a process that makes educated projections using previous data as inputs when identifying the direction of future trends. Forecasting future events in the real world, including pandemics, the economy, or the environment, is still complex but essential. Because dynamic information processing is a crucial component of efficient forecasting, AI researchers are considering using strong large-scale language models to automate these processes.
Researchers present a dataset with tens of thousands of forecasting questions and a date-based news corpus in the new paper Forecasting Future World Events with Neural Networks. They also curate IntervalQA, a dataset with numerical questions and metrics for calibration.
Continue reading | Checkout the paper and github
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Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes during training. In this post […]
The post Using Learning Rate Schedules for Deep Learning Models in Python with Keras appeared first on Machine Learning Mastery.
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A one-of-a-kind electric race car revved to life before it was manufactured — or even prototyped — thanks to GPU-powered extended reality technology. At the Automotive Innovation Forum in May, NVIDIA worked with Autodesk VRED to showcase a photorealistic Porsche electric sports car in augmented reality, with multiple attendees collaborating in the same immersive environment. Read article >
The post No Fueling Around: Designers Collaborate in Extended Reality on Porsche Electric Race Car appeared first on NVIDIA Blog.
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What is document management?
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The role of AI in the Metaverse has yet to be established. Is AI and blockchain technology a good fit?
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Optical character recognition (OCR) is the task of converting printed or handwritten text into machine-encoded text. OCR has been widely used in various scenarios, such as document electronization and identity authentication. Because OCR can greatly reduce the manual effort to register key information and serve as an entry step for understanding large volumes of documents, […]
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https://youtu.be/jSdHmImyUjk
Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time.
OUTLINE:
0:00 - Introduction
2:00 - Main Contributions
5:45 - Mode 1 and Mode 2 actors
15:40 - Self-Supervised Learning and Energy-Based Models
20:15 - Introducing latent variables
25:00 - The problem of collapse
29:50 - Contrastive vs regularized methods
36:00 - The JEPA architecture
47:00 - Hierarchical JEPA (H-JEPA)
53:00 - Broader relevance
56:00 - Summary & Comments
Paper: https://openreview.net/forum?id=BZ5a1r-kVsf
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I recently noticed a Weibo (Chinese Twitter) thread of an alarming potential academic misconduct - Prof. Yisen Wang’s girlfriend accused him of cheating and collusion behaviors in recent top-tier machine learning conferences, including but may not limit to NeurIPS2021 and ICML2021. Yisen Wang (homepage: https://yisenwang.github.io/) obtained his Ph.D. degree at Tsinghua University (China) and is now an assistant professor at Peking University (China). Yisen is interested in adversarial attack, etc.
Here are some facts from Yisen’s girlfriend’s post:
[Cheating in best paper nomination in ICML 2021] In ICML2021, Yisen asked one area chair of ICML2021 to recommend his first PhD student Jingyi Cui’s paper to be best paper candidate(I am not sure if it is termed as “best paper candidate”, …
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Almost all industries are now using machine learning systems to improve the efficiency and dependability of their work. With the increasing use of ML, companies have seen a boom in the investments in the resources needed to support ML systems. Additionally, a single ML process necessitates the execution of numerous distinct models, further complicating the process and increasing costs.
The idea of “Unified Models” was established in recent years, where a single model is constructed to power a process or product rather than a collection of connected but independent models. Combining all of the necessary data into one array and passing it to the model makes it possible to create a unified model that delivers all of the findings at once rather than by calling individual models one at a time.
Continue reading | Check out the demo
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In short: Data Engineers are still the most sought-after professionals in the field (more engineering, less "modeling"?), demand for analysts and leadership (!) roles is on the rise.
Full insights here: https://insights.ai-jobs.net/the-10-most-in-demand-jobs-in-ai-ml-and-big-data-in-2022/
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Let’s say you have identified a use case in your organization that you would like to handle via a chatbot. You familiarized yourself with Amazon Lex, built a prototype, and did a few trial interactions with the bot. You liked the overall experience and now want to deploy the bot in your production environment, but […]
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Machine learning (ML) is disrupting a lot of industries at an unprecedented pace. The healthcare and life sciences (HCLS) industry has been going through a rapid evolution in recent years embracing ML across a multitude of use cases for delivering quality care and improving patient outcomes. In a typical ML lifecycle, data engineers and scientists […]
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NVIDIA’s latest corporate responsibility report shares our efforts in empowering employees and putting to work our technologies for the benefit of humanity. Amid ongoing global economic concerns and pandemic challenges, this year’s report highlights our ability to attract and retain talent that come here to do their life’s work while tackling some of the world’s Read article >
The post Mission-Driven: Takeaways From Our Corporate Responsibility Report appeared first on NVIDIA Blog.
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Nothing beats the summer heat like GFN Thursday. Get ready for four new titles streaming at GeForce quality across nearly any device. Buckle up for some great gaming, whether poolside, in the car for a long road trip, or in the air-conditioned comfort of home. Speaking of summer, it’s also last call for this year’s Read article >
The post GFN Thursday Brings New Games to GeForce NOW for the Perfect Summer Playlist appeared first on NVIDIA Blog.
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Want to learn about AI and machine learning? There are plenty of resources out there to help — blogs, podcasts, YouTube tutorials — perhaps too many. Machine learning engineer Santiago Valdarrama has taken a far more focused approach to helping us all get smarter about the field. He’s created a following by posing one machine Read article >
The post Wordle for AI: Santiago Valderrama on Getting Smarter on Machine Learning appeared first on NVIDIA Blog.
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One the biggest stories of the year in the AI community is about a Google engineer’s claim of sentient AI. This was part of Google’s LaMDA…
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Artificial intelligences as our allies
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Over the coming decade, deep learning looks set to have a transformational impact on the natural sciences. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. Could this capability represent the dawn of a new paradigm of scientific discovery? […]
The post AI4Science to empower the fifth paradigm of scientific discovery appeared first on Microsoft Research.
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Researchers develop a comfortable, form-fitting fabric that recognizes its wearer’s activities, like walking, running, and jumping.
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Tried to explain as much as possible in the title. I did a "run" of DALL-E and I have already used photoshop's macros to crop each of them in a different file bc I feel like there's an interesting experience in watching it go through similar but different iteractions, but I would like it to be sorted by similarity to make the most impact. Can any of you recommend me a way to do that? The first result I found in google pinged the antivirus so I felt like getting recommendations was the way to go.
Here's an example of that kind of images I'm talking about https://imgur.com/a/miG2WWZ
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View the tutorial here: HERE
This tutorial teaches you how to transfer the style of one image to another image using neural-style-pt.
Below is a imgur gallery showing off the transformation process.
https://imgur.com/gallery/iMlkkQi
Let me know if you have any questions or comments.
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Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens […]
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As the title suggests, I would like to know the correct way to pre-process the cityscapes dataset for object detection. There are multiple ways how this can be done. There is a version in Detectron2, in MM Detection, there is this. Which one is the correct way, without getting errors in the labels? Anybody worked with this before? Would be glad if anybody might have an idea.
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Machine learning solutions are already embedded in the finance and banking industry. In this article, we reviewed the most popular use cases of ML in banking and shared practical tips on how to implement it into your business.https://exadel.com/news/how-machine-learning-is-used-in-finance-and-banking
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Per his tweet at https://twitter.com/goodfellow_ian/status/1544638709039091717, Goodfellow will be a research scientist under Oriol Vinyals' Deep Learning team.
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Hi all,
I've been an active practitioner in Deep Learning and then wanted to build something in MLOps.
So wanted to dig deeper in how DevOps evolved and wanted to check if MLOps can take the same path.
The findings are really great. Absolutely every tool doing well in the market is a clear replacement for DevOps tool in MLOps.
Here is my blog on it. Looking for feedback. If you have any comments, let me know. Will add them.
https://sachinchandra.substack.com/p/bringing-software-development-principles
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AI has brought a new life to art.
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Piction Health, founded by Susan Conover SM ’15, uses machine learning to help physicians identify and manage skin disease.
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An interesting article in the Systematic Biology journal about identifying insects: https://academic.oup.com/sysbio/article/68/6/876/5368535
See as well: Deep learning and computer vision will transform entomology
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Prompted by a recent discussion on social media, I did some benchmarks and wrote down my thoughts on why it doesn't really make a difference whether we choose batch sizes as powers of 2: https://sebastianraschka.com/blog/2022/batch-size-2.html
What is your experience, do you
do you stick to batch sizes as powers of 2 or do you choose batch sizes more freely?
notice a substantial difference when you choose batch sizes as powers of 2 (or multiples of 8)?
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Keras is a Python library for deep learning that wraps the efficient numerical libraries TensorFlow and Theano. Keras allows you to quickly and simply design and train neural network and deep learning models. In this post you will discover how to effectively use the Keras library in your machine learning project by working through a […]
The post Binary Classification Tutorial with the Keras Deep Learning Library appeared first on Machine Learning Mastery.
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A simple and powerful regularization technique for neural networks and deep learning models is dropout. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. After reading this post you will know: How the dropout regularization technique works. How to use dropout on […]
The post Dropout Regularization in Deep Learning Models With Keras appeared first on Machine Learning Mastery.
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If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment […]
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Socially disadvantaged communities have often raised legitimate concerns about being over-policed and under-protected. Now, the rise of AI…
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Since a customized AI solution is always individual, no one can give you a general cost estimate.
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Putting art, mathematics and computers together in the mid-1980s created a new genre of digital media: fractal art. In the NVIDIA Studio this week, computer graphics (CG) artist, educator and curator Xueguo Yang shares his insights behind fractal art — which uses algorithms to artistically represent calculations derived from geometric objects as digital images and animations.
The post Computer Graphics Artist Xueguo Yang Shares Fractal Art Series This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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The study on explainability or explainable AI is currently receiving a lot of attention as DNNs become accessible in a variety of application domains. Many explainability techniques that attempt to provide the local explanation of the DNNs prediction for a particular instance, such as techniques that provide saliency maps for understanding which sub-parts in an instance are most responsible for the model prediction, have been proposed in an effort to open the black box of DNNs.
While local explanation techniques have seen a rapid growth in research in recent years, the majority of attention has been placed on handling the generation of explanations rather than understanding whether the explanations are accurate or reasonable, what to do if they are, and how to modify the model to produce more accurate or reasonable explanations.
Continue reading | Checkout the paper and github
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Artificially intelligent models have recently advanced to the point that users will soon be able to utilize these models to immediately construct and alter nearly photorealistic three-dimensional sceneries from the comfort of their laptops. Since these technologies make it simple to generate hyperrealistic avatars, they will revolutionize the way artists working on video games and CGI for movies approach their work. For quite some time, AIs have been able to create realistic 2D images. However, 3D scenarios have proven to be more challenging due to the enormous computer power needed. The AI model EG3D, created by a team of Stanford academics, can be used to produce random high-resolution images of faces and other things having an underlying geometric structure. This model is one of the first 3D models now in use to reach rendering quality close to photorealism.
Continue reading | Checkout the paper, github
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https://codingvidya.com/best-artificial-intelligence-courses-for-healthcare/
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A blog about representation learning from masked images, what makes a good mask, and how to learn such masks: https://akosiorek.github.io/ml/2022/07/04/masking_repr_learning_vision.html.
Based on a recent ICML paper: Shi et. al, "Adversarial Masking for Self-Supervised Learning", ICML 2022.
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Hi all. Created a basic guide on generating AI art using VQGAN+CLIP. This is for biginners:
VQGAN - A step-by-step guide
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Active research projects frequently devolve into a jumble of files with varying degrees of descriptive names processed by Python programs and bound together by Bash scripts. People can never be entirely sure that they can actually repeat a result since intermediate outcomes disappear or become difficult to locate.
Tango ensures you never operate on outdated data by taking care of your intermediate and final outcomes and finding them again when needed.
What does that actually mean?
Tango has a lot of capabilities, but its main feature is this:
Tango caches function results even if your process is restarted. If one merely takes advantage of one function, Tango can significantly benefit you.
Continue reading | Github
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I want to share the PyTorch implementation of "An Improved One millisecond Mobile Backbone" paper.
Unfortunately, I don't have the appropriate computational resources to train the models on ImageNet, so feel free to use my implementation for that purpose.
Hope you all find it useful, feedback would be appreciated.
Repository: https://github.com/federicopozzi33/MobileOne-PyTorch
Paper: https://arxiv.org/abs/2206.04040
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Hey, I wanted to share my recent ML project: LCPN-hiernet.
LCPN-hiernet is a hierarchical image classification model for e-commerce items based on EfficientNet-b4 and LCPN (Local Classifier per Parent Node) technique.
LCPN technique is training one multi-class classifier for each parent node, to distinguish between its child nodes. In my example of classifying fashion products, that would mean one classifier on the first level (to determine “bags”, “clothes” or “accessories”), then three more classifiers to determine the specific model.
I’m sure there are a lot of places to improve on, and I would really appreciate anyone’s feedback or suggestions on how I can improve!
Github Repo
Project Page
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Tensors are an effective method for handling and representing multidimensional data arrays. However, they have a limitation in terms of storage and computation. Tensor decompositions are crucial in machine learning because they factorize the weights of neural networks. This research introduces tntorch, an open-source python package for tensor learning that supports several decompositions through a single user interface. In contrast to the state-of-the-art packages, tntorch emphasizes an easy-to-use, decomposition-independent interface inherited from PyTorch.
🚦 An open-source python package for tensor learning that supports several decompositions through a single user interface
🚦 In contrast to the state-of-the-art packages, tntorch emphasizes an easy-to-use, decomposition-independent interface inherited from PyTorch
🚦 Several decomposition models that are crucial in machine learning, such as CANDEDOMP/ PARAFAC (CP), the Tucker decomposition, and the tensor train (TT), is supported by tntorch
🚦 It gives machine learning access to the power of low-rank tensor decompositions while maintaining the excellent appearance and feel of PyTorch tensors
Continue reading | Checkout the paper and github
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An in-depth analysis about regulations for AI in medical devices.
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Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train. In this post you will discover how you can use the grid […]
The post How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras appeared first on Machine Learning Mastery.
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AI Weirdness: the strange side of machine learning
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Anomalib is Machine Library developed by AI researchers from Intel which implements state of the art algorithms for anomaly detection. Anomaly detection is popular use case in the industrial sector and such algorithms can help provide real-time feedback to manufactures on how well their production lines are performing.
Anomaly Detection is a challenging problem often due to a biased dataset. Anomalous images can be scare therefore these algorithms are trained on good images in an unsupervised fashion. By learning the normality, upon inference, the models can detect whether images are anomalous or not.
Anomalib was built using a PyTorchLightning Backbone and offers an easy way to deploy the models with OpenVino for inference speedup.
Link to the github repo: https://github.com/openvinotoolkit/anomalib
Link to a tutorial on how to train your custom dataset with anomalib: https://github.com/openvinotoolkit/anomalib/tree/development/docs/blog/001-train-custom-dataset
Please feel free to check out the repo and give us your feedback
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You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for […]
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In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API […]
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Some things are easy as A, B, C. But when it comes to autonomous vehicles, the key may be in one, two, three. Faction, a Bay Area-based startup and NVIDIA Inception member, is preparing to debut its business-to-business autonomous delivery service, accelerating its commercial deployment with three-wheel production electric vehicles purpose-built for driverless services. In Read article >
The post Three Wheeling: Startup Faction Develops Affordable Tri-Wheel AVs on NVIDIA DRIVE appeared first on NVIDIA Blog.
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Turn the TV on. GeForce NOW is leveling up gaming in the living room. The Samsung Gaming Hub launched today, delivering GeForce NOW natively on 2022 Samsung Smart TVs. Plus, the SHIELD Software Experience Upgrade 9.1 is now rolling out to all NVIDIA SHIELD TVs, delivering new gaming features that improve GeForce NOW. Great living Read article >
The post The Gaming Evolution Will Be Televised: GFN Thursday Levels Up the Living Room Experience on New Samsung TVs and More appeared first on NVIDIA Blog.
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Blog post miniseries summarizing byteLAKE’s recommendation about hardware platforms to perform CFD Suite’s AI Training at the Edge.
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Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for end users.
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RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on […]
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Online conversations are ubiquitous in modern life, spanning industries from video games to telecommunications. This has led to an exponential growth in the amount of online conversation data, which has helped in the development of state-of-the-art natural language processing (NLP) systems like chatbots and natural language generation (NLG) models. Over time, various NLP techniques for […]
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Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]
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With the growth in adoption of online applications and the rising number of internet users, digital fraud is on the rise year over year. Amazon Fraud Detector provides a fully managed service to help you better identify potentially fraudulent online activities using advanced machine learning (ML) techniques, and more than 20 years of fraud detection […]
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If you’ve looked at Keras models on Github, you’ve probably noticed that there are some different ways to create models in Keras. There’s the Sequential model which allows you to define an entire model in a single line, usually with some line breaks for readability, then there’s the functional interface that allows for more complicated […]
The post Three Ways to Build Machine Learning Models in Keras appeared first on Machine Learning Mastery.
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Silicon Valley magic met Wednesday with 175 years of industrial technology leadership as Siemens CEO Roland Busch and NVIDIA Founder and CEO Jensen Huang shared their vision for an “industrial metaverse” at the launch of the Siemens Xcelerator business platform in Munich. “When we combine the real and digital worlds we can achieve new levels Read article >
The post The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach appeared first on NVIDIA Blog.
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NVIDIA and its partners continued to provide the best overall AI training performance and the most submissions across all benchmarks with 90% of all entries coming from the ecosystem, according to MLPerf benchmarks released today. The NVIDIA AI platform covered all eight benchmarks in the MLPerf Training 2.0 round, highlighting its leading versatility. No other Read article >
The post NVIDIA, Partners Show Leading AI Performance and Versatility in MLPerf appeared first on NVIDIA Blog.
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The June NVIDIA Studio Driver is available for download today, optimizing the latest creative app updates, all with the stability and reliability that users count on. Creators with NVIDIA RTX GPUs will benefit from faster performance and new features within Blender version 3.2, BorisFX Sapphire release 2022.5 and Topaz Denoise AI 3.7.0.
The post NVIDIA Studio Driver Elevates Creative Workflows in Blender 3.2, BorisFX Sapphire and Topaz Denoise AI appeared first on NVIDIA Blog.
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Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society.
The post Introducing the Microsoft Climate Research Initiative appeared first on Microsoft Research.
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I read a super interesting KDD 2022 paper recently - "Learning Backward Compatible Embeddings".
The paper tackles a common industry problem of ensuring compatibility of newer embeddings with an older downstream model.
An annotated version of the paper - Annotated-ML-Papers/Learning Backward Compatible Embeddings.pdf
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How do pro-choice vs. pro-life twitter users differ?
I built a free, labeled dataset of #RoeVsWade tweets, and an ML classifier on top.
Some insights:
Pro-life users are 20.4x more likely to put "christ" and 16.1x more likely to put "maga" in their bio.Pro-choice users are 7.5x more likely to put "blm" and 6.5x more likely to put "she/her".
Full analysis + link to raw dataset here.
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I read a super interesting KDD 2022 paper recently - "Learning Backward Compatible Embeddings".
The paper tackles a common industry problem of ensuring compatibility of newer embeddings with an older downstream model.
An annotated version of the paper - Annotated-ML-Papers/Learning Backward Compatible Embeddings.pdf
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Transformers are awesome for so many things in 2022, but one thing I've found them to struggle with is generating embeddings for long documents.
I put together a blog post going through some interesting techniques. Let me know if it helped you!
Blog post
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Just released. Quaterion — an open source framework for training and fine-tuning similarity learning models. It enables you to train models significantly (100x) faster, and iterate over experiments in minutes instead of hours even with a laptop GPU. It takes advantage of the PyTorch Lightning backend to make a flexible and scalable learning pipeline. GitHub https://github.com/qdrant/quaterion
Here is a demo of the caching functionality.
https://i.redd.it/9qi8gf9n4d891.gif
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Developers can use RestifyML to
Create DataScience experiments
Create Data Source and upload CSV data within the experiment
Do Data Cleansing and Sanitization
Visualize raw data using Data Exploration
Select Features which would help in building models
Build Model, save or export them
Finally, deploy Model and expose them as REST API
Consume Machine Learning REST API from any Application
Profit!
https://github.com/rebataur/RestifyML
Feedback/ Feature Request appreciated.
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Jupyter Notebook: https://colab.research.google.com/drive/1BsVkddtVMX35aZAvo2GyI-wSFPVBCWuA
Github: https://github.com/facebookresearch/torchdim
Some tweet threads about it
Mine: https://twitter.com/cHHillee/status/1541536627746426881
Sasha Rush: https://twitter.com/srush_nlp/status/1541526906113298433
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The second AI Policy Forum Symposium convened global stakeholders across sectors to discuss critical policy questions in artificial intelligence.
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Amazon Polly is a leading cloud-based service that converts text into lifelike speech. Following the adoption of Neural Text-to-Speech (NTTS), we have continuously expanded our portfolio of available voices in order to provide a wide selection of distinct speakers in supported languages. Today, we are pleased to announce four new additions: Pedro speaking US Spanish, […]
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Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, […]
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In computer vision, semantic segmentation is the task of classifying every pixel in an image with a class from a known set of labels such that pixels with the same label share certain characteristics. It generates a segmentation mask of the input images. For example, the following images show a segmentation mask of the cat […]
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Every business needs the ability to predict the future accurately in order to make better decisions and give the company a competitive advantage. With historical data, businesses can understand trends, make predictions of what might happen and when, and incorporate that information into their future plans, from product demand to inventory planning and staffing. If […]
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Taiwan has nearly 85,000 kidney dialysis patients — the highest prevalence in the world based on population density. Taipei Veterans General Hospital (TVGH) is working to improve outcomes for these patients with an AI model that predicts heart failure risk in real time during dialysis procedures. Cardiovascular disease is the leading cause of death for Read article >
The post Detect to Protect: Taiwan Hospital Deploys Real-Time AI Risk Prediction for Kidney Patients appeared first on NVIDIA Blog.
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Moderation is the process of controlling the wanted contents from the online platforms like social media networking sites. And it is…
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“Homer Simpson reacting to the crash of Bitcoin”
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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View the tutorial here: HERE
This tutorial teaches you how to convert any text prompt to an image using VQGAN-Clip.
For example you could use the prompt "A spray painting of a waiting computer and a bedroom in the style of Edgar Degas and Art Nouveau".
This would generate the following image:
https://imgur.com/J3qGlc4
Let me know if you have any questions or comments.
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With a potential recession lurking on the horizon, 99% of companies will make the same old “safe” mistakes: hunker down, let people go, shrink, and hope to hold on for dear life. However, growth-oriented organizations will see this as a business opportunity – an opportunity to leverage their data to “do more with less”. You… Read More »Data & Analytics Regression Playbook: Make Your Data Work Harder…And Smarter!
The post Data & Analytics Regression Playbook: Make Your Data Work Harder…And Smarter! appeared first on Data Science Central.
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We're used to finding that task performance scales well with large increases in sizes of language models. But for real-world applications, it's also very meaningful to search for failure cases preemptively to fix the underlying issues. Can you find and convincingly demonstrate these failure cases where language models scale inversely, with larger models behaving worse?
You don't necessarily need to have extra deep knowledge of ML or language models in order to participate and win, because all models are frozen and you only need to come up with the right data.
Check out these resources to learn more! Announcement Twitter thread, contest details on Github. The deadline for the first round of the contest is August 27, 2022.
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Surprised I haven't seen more chatter about this. What do you think about Nvidia's instant Nerf which turns 2d into 3d based on these techniques https://arxiv.org/abs/2003.10016
Does the output of a NeRF give a depth map that's comparable to what you'd get from a Kinect?
Can these be used to create 3D models one would use in Unreal or Blender?
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Hey r/MachineLearning! We are the co-founders of Stack, a hub for data collaboration and versioning. We are developing this tool to help ML teams automatically track changes in their data seamlessly.
We are opening a waiting list for our beta, which we aim to release soon. You can sign up at: https://www.getstack.ai/
We are also actively looking for feedback. Feel free to share any comments or thoughts!
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I am trying to train a latent-diffusion model by following the instructions on the repo, however I am running into errors while sampling from the checkpointed models. Can someone help?
I am getting Errors while trying to sample using sample_diffusion.py from a custom model trained on LSUN churches
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We usually use TensorFlow to build a neural network. However, TensorFlow is not limited to this. Behind the scene, TensorFlow is a tensor library with automatic differentiation capability. Hence we can easily use it to solve a numerical optimization problem with gradient descent. In this post, we are going to show how TensorFlow’s automatic differentiation […]
The post Using autograd in TensorFlow to Solve a Regression Problem appeared first on Machine Learning Mastery.
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View the tutorial here: HERE
This tutorial teaches you how to convert any text prompt to an image using VQGAN-Clip.
For example you could use the prompt "A spray painting of a waiting computer and a bedroom in the style of Edgar Degas and Art Nouveau".
This would generate the following image:
https://imgur.com/J3qGlc4
Let me know if you have any questions or comments.
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Object localization trained from scratch for emoji dataset in TensorFlow 2.8. Getting an IoU = 0.5969 and classification output accuracy = 100%. The code can be referred here. Though in fairness, I am using only 9 classes out of the emoji dataset. Thoughts?
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Launched at AWS re:Invent 2021, Amazon SageMaker Ground Truth Plus helps you create high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. All you do is share data along with labeling requirements, and Ground Truth Plus sets up and manages your data labeling workflow […]
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maybe you haven’t heard this, this is voice https://youtu.be/FAvcn_8OuMk this sound is really good. im wondering if anyone knows which al is used for text to voice?
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We are happy to invite you to the Hugging Face Gradio CVPR event - a community event in which we will create interactive demos for CVPR papers. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event is open until June 30th, 2022 (AOE Time Zone). We are organizing this event on Huggingface: https://huggingface.co/CVPR. Prizes will be given at the end of the event.
Demos will be built with Gradio and we encourage using the new Gradio Blocks API. Blocks allows you to build web-based demos in a flexible way using the Gradio library. Gradio is a popular choice for building demos for machine learning models, as it allows you to create web-based UIs all in Python. For example, here is a Gradio Demo for FLAVA: A Foundational Language And Vision Alignment Model:
https://reddit.com/link/vkqmhu/video/48cnmkfiku791/player
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Data annotation is at the forefront of the recent revolution in healthcare AI, driving continuous progress in the field through continuous innovation through the idea of Artificial Intelligence. A computer program can use human intelligence to perform many tasks that humans carry out today. The concept is called artificial intelligence (AI). Finding tumors, discovering kidney… Read More »Datasets and Data Annotation — The Building Blocks for Healthcare AI
The post Datasets and Data Annotation — The Building Blocks for Healthcare AI appeared first on Data Science Central.
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Here's the list of questions we covered.
❓Who will benefit the most from Google's Business Messages?
❓How does Google's Business Messages differ from other solutions? (Like WhatsApp)
❓What are the most beneficial features of Google's Business Messages? ❓And for pre-purchase research/pre-sales product support?
❓What problems businesses can solve and better not solve using Google's Business Messages?
❓What questions/experience should be automated and what it's better to handle with agents?
❓What are the first steps to integrate Google's Business Messages into customer experience (CX) strategy and workflows?
❓How do you keep the human touch when automation is involved?
❓What are some "rookie mistakes" when it comes to implementing Google's Business Messages?
If you found a question you're interested in, here's the link where you can read some insights and listen to the episode.
Hope you'll enjoy our conversation!
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Hey, I wanted to share with you a podcast on implementing AI-based self-checkout like Amazon. Stores where shoppers can enter, select items and simply leave the store without having to queue. Everything happens automatically. The speakers discuss how difficult it is to implement this.
https://youtu.be/HV4IfiQjRTo
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Hey, check out our (!) video (parody) that presents how our E2V-SDE paper (that has been accepted to CVPR 2022) largely consists of texts that are uncredited verbatim copies from more than 10 previously published papers. Enjoy!
https://youtube.com/watch?v=UCmkpLduptU
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Today, we’re excited to announce that Amazon Forecast offers the ability to generate forecasts on a selected subset of items. This helps you to leverage the full value of your data, and apply it selectively on your choice of items reducing the time and effort to get forecasted results. Generating a forecast on ‘all’ items of the […]
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The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post. As more organizations use deep learning techniques such as computer vision and natural language processing, the machine learning (ML) developer persona needs scalable tooling around experiment tracking, lineage, and […]
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This blog post is co-authored by Guillermo Ribeiro, Sr. Data Scientist at Cepsa. Machine learning (ML) has rapidly evolved from being a fashionable trend emerging from academic environments and innovation departments to becoming a key means to deliver value across businesses in every industry. This transition from experiments in laboratories to solving real-world problems in […]
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In a previous post, we talked about analyzing and tagging assets stored in Veeva Vault PromoMats using Amazon AI services and the Veeva Vault Platform’s APIs. In this post, we explore how to use Amazon AppFlow, a fully managed integration service that enables you to securely transfer data from software as a service (SaaS) applications […]
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As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and […]
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We are excited to announce Amazon CodeWhisperer, a machine learning (ML)-powered service that helps improve developer productivity by providing code recommendations based on developers’ natural comments and prior code. With CodeWhisperer, developers can simply write a comment that outlines a specific task in plain English, such as “upload a file to S3.” Based on this, […]
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Running machine learning (ML) experiments in the cloud can span across many services and components. The ability to structure, automate, and track ML experiments is essential to enable rapid development of ML models. With the latest advancements in the field of automated machine learning (AutoML), namely the area of ML dedicated to the automation of […]
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Over recent years, web scraping has become an incredibly popular practice, the rise of this field being largely attributed to the vast amounts of data that are produced and distributed every single day.
The post 5 Most Common Use Cases for Web Scraping appeared first on Data Science Central.
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You may not know of Todd Mozer, but it’s likely you have experienced his company: It has enabled voice and vision AI for billions of consumer electronics devices worldwide. Sensory, started in 1994 from Silicon Valley, is a pioneer of compact models used in mobile devices from the industry’s giants. Today Sensory brings interactivity to Read article >
The post Finding NeMo: Sensory Taps NVIDIA AI for Voice and Vision Applications appeared first on NVIDIA Blog.
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To foster climate action for a healthy global environment, NVIDIA is working with the United Nations Satellite Centre (UNOSAT) to apply the powers of deep learning and AI. The effort supports the UN’s 2030 Agenda for Sustainable Development, which has at its core 17 interrelated Sustainable Development Goals. These SDGs — which include “climate action” Read article >
The post UN Satellite Centre Works With NVIDIA to Boost Sustainable Development Goals appeared first on NVIDIA Blog.
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MIT alumni-founded Overjet analyzes and annotates dental X-rays to help dentists offer more comprehensive care.
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Deep learning models are often complex and mostly uninterpretable.
• One strategy is to learn the nonlinear relation of features.
But, there are so many features to learn from:
• Research shows a set of important features can improve the learning process.
• So let's focus on the most correlated features.
Paper📜: https://arxiv.org/abs/2203.04383
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PR Announcement: https://medium.com/yandex/yandex-publishes-yalm-100b-its-the-largest-gpt-like-neural-network-in-open-source-d1df53d0e9a6
Github: https://github.com/yandex/YaLM-100B
Network is trained using same principles as Megatron LM, inference alone will require 4 A100s
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The plugin provides a DirectML PluggableDevice backend for TensorFlow 2, so any GPU which supports DirectX 12 should be able to work with TF2. Hopefully this will pave the way for more support for non-NVIDIA GPUs in ML.
They provide some more details (installation, code samples, etc') in the Windows AI devblog.
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Seems almost all the examples of text-to-image are based on tiny prompts with very few details ("avocado chair").
Do any such systems do a good job at keeping track of details - like the first 2 paragraphs of The Hobbit and correctly place the "polished chairs", "pegs for hats and coats", and "deep-set round windows looking over his garden, and meadows beyond, sloping down to the river"?
Assuming they don't - what approach(es) might make sense to design such systems?
I'm speculating that you'd need much larger embedding vectors (to correctly connect concepts from the right adjectives to the right nouns); and it'd be harder to find training data (perhaps frames of movies from novels would be a good source)?
Any pointers to anything in that direction?
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Apologies if this isn't the right place to ask. But I'm currently studying point cloud-based networks like pointcloud++, and all the related 3d object detection networks like pointpillars, voxelnet, etc. While I (think) understand the algorithms like feature propagation in pointnet++. I'm having trouble understanding how would one implement them. Or Where could I learn about writing operations in cuda and making sure they are compatible with backprop?
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AI generated images from text prompts are making the rounds with Dalle mini and DALLE.2. These systems are so powerful that people are admitting they cannot tell real from fake images anymore.
Google's LaMDA is producing conversational text chats that are so realistic that they spawned entire subreddits where users claim the software agent has become sentient.
So where is the instrumental and orchestral music that is indifferentiable from human composers?
In recent months I had heard some song continuations, where an AI was trained on the wave form of popular music, which was asked to continue. Those were fine, but ended up sounding like strange incoherent fever dreams. I fiddled with some midi-like continuations on a website. The output was janky, repetitive, and obviously computer-…
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Finally, there’s a family car any kid would want to be seen in. Beijing-based startup Li Auto this week rolled out its second electric vehicle, the L9. It’s a full-size SUV decked out with the latest intelligent driving technology. With AI features and an extended battery range of more than 800 miles, the L9 promises Read article >
The post Family Style: Li Auto L9 Brings Top-Line Luxury and Intelligence to Full-Size SUV With NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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Thanks to the GeForce cloud, even Mac users can be PC gamers. This GFN Thursday, fire up your Macbook and get your game on. This week brings eight more games to the GeForce NOW library. Plus, members can play Genshin Impact and claim a reward to start them out on their journeys streaming on GeForce Read article >
The post Making an Impact: GFN Thursday Transforms Macs Into GeForce Gaming PCs appeared first on NVIDIA Blog.
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Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations with a multi-account architecture want to avoid situations where they must extract data from one […]
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Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]
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We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over
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They make restaurant recommendations, help us pay bills, and remind us of appointments. Many people have come to rely on virtual assistants and chatbots to perform a wide range of routine tasks. But what if a single dialog agent, the technology behind these language-based apps, could perform all these tasks and then take the conversation […]
The post GODEL: Combining goal-oriented dialog with real-world conversations appeared first on Microsoft Research.
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We train the Deep reinforcement Learning model for IoT devices/Unmanned aerial vehicles at GPU and we have enough resources to train over there, what if we have to train that model on IoTs/UAVs, is it possible for UAV to compute that model?
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In this article, let’s discuss one of the trendy and handy web-scraping tools, Octoparse, and its key features and how to use it for our data-driven solutions. Hope you all are familiar with “WEB SCRAPING” techniques, and the captured data has been used to analyze business perceptions further. If you look at the end-end process… Read More »Exploring Octoparse for Data Preparations and Product Assessment
The post Exploring Octoparse for Data Preparations and Product Assessment appeared first on Data Science Central.
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Batch indexing into multidimensional tensors/arrays is kind of tricky, I made this project explaining the builtin syntax and also made wrappers for simplifying the interface, with additional features for underlying coordinate grid data (like signed distance functions) that need to be indexed by coordinate value rather than integer indices directly https://github.com/LemonPi/multidim_indexing
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Google published results from an seq2seq transformer model for autoregressive image generation.
Website: https://parti.research.google/
Paper: https://gweb-research-parti.web.app/parti_paper.pdf
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We are researchers at Carnegie Mellon University studying how software developers identify and act on ethical concerns at work. If you’re interested in helping us advance research in software ethics, please fill out this survey and we’ll reach out to you for a quick interview!
P.S.
You can check out this Stack Overflow blog post to read more about the direction of our research.
Anything you disclose to us during the survey / interview may appear in our study but will not be traceable to you.
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According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we’re still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements in deep learning in computer vision. […]
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Deep learning models with billions of parameters are trained through gradient-based stochastic optimization, thanks to powerful algorithms, systems, and hardware advancements. These algorithms include several hyperparameters that are essential for effective performance. Hyperparameter adjustment is required to control the behavior of a machine learning model. If our hyperparameters are not correctly set, our anticipated model parameters will not minimize the loss function, resulting in poor results. The lousy result suggests that our model has further faults. In actuality, the accuracy or confusion matrix will be worse.
Many hyperparameters exist like learning rate, regularisation type, degree, and size of neural network layers. Automating the setting of these hyperparameters and accelerating the training of neural network weights are necessary if domain experts and industry practitioners benefit from the most recent deep learning technologies. Even for specialists, tuning them takes a lot of time and effort; choosing the best hyperparameter configuration frequently depends on factors like cost or latency.
Continue reading | Checkout the paper, github
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A camera begins in the sky, flies through some trees and smoothly exits the forest, all while precisely tracking a car driving down a dirt path. This would be all but impossible in the real world, according to film and photography director Brett Danton.
The post Meet the Omnivore: Director of Photography Revs Up NVIDIA Omniverse to Create Sleek Car Demo appeared first on NVIDIA Blog.
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It may seem intuitive that AI and deep learning can speed up workflows — including novel drug discovery, a typically years-long and several-billion-dollar endeavor. But professors Artem Cherkasov and Olexandr Isayev were surprised to find that no recent academic papers provided a comprehensive, global research review of how deep learning and GPU-accelerated computing impact drug Read article >
The post Artem Cherkasov and Olexandr Isayev on Democratizing Drug Discovery With NVIDIA GPUs appeared first on NVIDIA Blog.
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Robotics Process Automation (RPA) is all about incorporating solutions that handle repetitive tasks faster and more efficiently. These…
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Specialization Intro video: https://youtu.be/g7dv-Lnuor4
Specialization on Coursera: https://www.coursera.org/specializations/machine-learning-introduction
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Hi
So we can finally play around with the cool NVLabs EG3D, but they refuse to release the inversion script.
Does anyone have success to pass a image and reconstruct a face in this project?
I am not having success when trying to do this, so I would greatly appreciate if anyone could share how to do it or if you know of an existing fork?
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Amazon Web Services and Udacity are partnering to offer free services to educate developers of all skill levels on machine learning (ML) concepts with the AWS Machine Learning Engineer Scholarship program. The program offers free enrollment to the AWS Machine Learning Foundations course and 325 scholarships awarded to the AWS Machine Learning Engineer Nanodegree, a […]
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Mangrove forests are an import part of a healthy ecosystem, and human activities are one of the major reasons for their gradual disappearance from coastlines around the world. Using a machine learning (ML) model to identify mangrove regions from a satellite image gives researchers an effective way to monitor the size of the forests over […]
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The increasing ubiquity of satellite data over the last two decades is helping scientists observe and monitor the health of our constantly changing planet. By tracking specific regions of the Earth’s surface, scientists can observe how regions like forests, water bodies, or glaciers change over time. One such region of interest for geologists is mangrove […]
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The Keras Python library for deep learning focuses on the creation of models as a sequence of layers. In this post you will discover the simple components that you can use to create neural networks and simple deep learning models using Keras from TensorFlow. Let’s get started. May 2016: First version Update Mar/2017: Updated example […]
The post How To Build Multi-Layer Perceptron Neural Network Models with Keras appeared first on Machine Learning Mastery.
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Early last year, our research team from the Visual Computing Group introduced Swin Transformer, a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important vision benchmark of COCO object detection and did so by a large margin. Convolutional neural networks (CNNs) have long been the architecture of […]
The post Swin Transformer supports 3-billion-parameter vision models that can train with higher-resolution images for greater task applicability appeared first on Microsoft Research.
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Google JAX is a powerful framework for machine learning that offers many benefits over other popular frameworks such as PyTorch and…
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Hello, i wanna create neural network that will read DMG dealt fields and output them from picture like this. So far i have 1677 of them (they are mostly 3 field but some have 2 or 1). Do you think its enough to label or should i gather more?
And one more question is if its good idea to try to train it on these pictures or should i split pictures so they are individual field of dmg dealt?
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MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
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Jazz is all about improvisation — and NVIDIA is paying tribute to the genre with AI research that could one day enable graphics creators to improvise with 3D objects created in the time it takes to hold a jam session. The method, NVIDIA 3D MoMa, could empower architects, designers, concept artists and game developers to Read article >
The post AI in the Big Easy: NVIDIA Research Lets Content Creators Improvise With 3D Objects appeared first on NVIDIA Blog.
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The metaverse is the next big step in the evolution of the internet — the 3D web — which presents a major opportunity for every industry from entertainment to automotive to manufacturing, robotics and beyond. That’s why NVIDIA is joining our partners in the Metaverse Standards Forum, an open venue for all interested parties to Read article >
The post NVIDIA Joins Forum to Help Lay the Foundation of the Metaverse appeared first on NVIDIA Blog.
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3D artist Jae Solina, who goes by the stage name JSFILMZ, steps In the NVIDIA Studio this week to share his unique 3D creative workflow in the making of Cyberpunk Short Film — a story shrouded in mystery with a tense exchange between two secretive contacts.
The post 3D Artist Jae Solina Goes Cyberpunk This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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The growing ubiquity of IoT and AI has left no industry untouched. Businesses have unlocked their transformational value in meeting the modern needs of consumers, with cloud computing posing as the key enabler and accelerator. Evidently, we are witnessing the action in a panoply of applications. Most evident are in supply chain innovation, healthcare IT,… Read More »AI and Blockchain Cloud Services Orchestrate Digital Business Transformation
The post AI and Blockchain Cloud Services Orchestrate Digital Business Transformation appeared first on Data Science Central.
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Salesforce has built an open-source machine learning framework called OmniXAI, which stands for Omni eXplainable AI. This library takes an “omni-directional” approach to XAI, with extensive interpretable ML features that address many problems with explaining ML model decisions in reality. OmniXAI is a one-stop comprehensive library that makes explainable AI accessible to academics requiring explanations for each stage of the machine learning process. This is not limited to data exploration, feature engineering, model development, evaluation, decision making, etc.
🚦 A one-stop solution for analyzing different stages in a standard ML pipeline in real-world applications.
🚦 Two types of explanations — local and global
🚦 Includes most popular explanation methods, such as feature-attribution/importance explanation (LIME [1], SHAP [2], Integrated Gradients (IG) [3], Grad-CAM [4], L2X), counterfactual explanation (MACE [5]), partial dependence plots (PDP), and model-specific methods (linear and tree models)
🚦 Can be applied on tabular, vision, NLP, and time-series tasks.
Continue reading | Checkout the paper, article, github, dashboard
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Salesforce has built an open-source machine learning framework called OmniXAI, which stands for Omni eXplainable AI. This library takes an “omni-directional” approach to XAI, with extensive interpretable ML features that address many problems with explaining ML model decisions in reality. OmniXAI is a one-stop comprehensive library that makes explainable AI accessible to academics requiring explanations for each stage of the machine learning process. This is not limited to data exploration, feature engineering, model development, evaluation, decision making, etc.
🚦 A one-stop solution for analyzing different stages in a standard ML pipeline in real-world applications.
🚦 Two types of explanations — local and global
🚦 Includes most popular explanation methods, such as feature-attribution/importance explanation (LIME [1], SHAP [2], Integrated Gradients (IG) [3], Grad-CAM [4], L2X), counterfactual explanation (MACE [5]), partial dependence plots (PDP), and model-specific methods (linear and tree models)
🚦 Can be applied on tabular, vision, NLP, and time-series tasks.
Continue reading | Checkout the paper, article, github, dashboard
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Changes the page CSS and text editor and generates Python code to change Matplotlib styles to match the theme the user choses. Users may import themes or use any of the 50+ provided. Colab Themes enhances the data science experience by transforming the way users view their code and their data!
Check it out on Github or install it via the Chrome Webstore
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https://preview.redd.it/efbfdsbhmo691.png?width=964&format=png&auto=webp&s=4593b345d28e393447c4cf66af2abdbca72309c9
Everywhere that I have read, Policy-Based methods are supposed to be more robust and converge faster than Value-Based methods.
Why does this table contradict that?
Edit:
Link to image: Atari games Benchmark (Atari Games) | Papers With Code
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This blog post is co-written with Nick Vargas and Anna Schreiber from Accenture. Scheduling customer appointments is often a manual and labor-intensive process. You can utilize advances in self-service technology to automate appointment scheduling. In this blog post, we show you how to build a self-service appointment scheduling solution built with Amazon Lex and Amazon […]
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If the King of Sweden wants help drafting his annual Christmas speech this year, he could ask the same AI model that’s available to his 10 million subjects. As a test, researchers prompted the model, called GPT-SW3, to draft one of the royal messages, and it did a pretty good job, according to Magnus Sahlgren, Read article >
The post The King’s Swedish: AI Rewrites the Book in Scandinavia appeared first on NVIDIA Blog.
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https://www.businessinsider.com/facebook-pytorch-beat-google-tensorflow-jax-meta-ai-2022-6
With companies and researchers leaving Tensorflow and going to PyTorch, Google seems to be interested in moving its products to JAX, addressing some pain points from Tensorflow like the complexity of API, and complexity to train in custom chips like TPU. The article says that JAX still has long way to go since it lacks proper optimization to GPUs and CPUs when compared to TPUs.
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This guy seems to be having a company that teaches AI-ethics to industry elites in Sweden.
https://pbs.twimg.com/profile_images/1231981924085882880/iM_9ACFb_400x400.jpg
He is also a plagiarist: https://andreasplagiarism.wordpress.com/2020/12/02/andreas-theodorou-committed-plagiarism-in-his-phd-thesis/
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🚦 HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218 4096×4096 images, and 104M 256 × 256 images
🚦 HIPT pushes the boundaries of both Vision Transformers and self-supervised learning in two important ways.
🚦 The code is available
Continue reading | Checkout the paper, github
https://i.redd.it/5jt6a83deg691.gif
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Defined as:
"a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher."
Keep in mind that a passive progress meter or a proficiency model does not qualify as an ITS.
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